Reconciling the Personalization-Privacy Paradox: Exploring Privacy Boundaries in Online Personalized Advertising

IF 7 3区 管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Journal of the Association for Information Systems Pub Date : 2022-01-01 DOI:10.17705/1jais.00775
Yu-Qian Zhu, Kritsapas Kanjanamekanant, Yi-Te Chiu
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引用次数: 1

Abstract

To reconcile the personalization-privacy paradox, we adopt the privacy as a state view and define privacy as a state of information boundary rule-following. We further identify five types of boundaries underlying some of the important implicit rules of maintaining privacy: communication channel, platform, device, temporal, and purpose boundaries. Using an online vignette survey, we investigated how each of these boundary types affected users’ privacy perceptions when they were subjected to personalized advertisements. Using fixed- and random-effects models, we investigated how violating different boundary rules leads to changes in perceived privacy. Our results show that all five boundary types are significant predictors of perceived privacy within individuals. The communication channel, device, and business versus private purpose are significant predictors of perceived privacy across the whole sample. Temporal boundaries and platform boundaries failed to achieve statistical significance when evaluated simultaneously with the other factors across the whole sample. This means that for each individual, observing the rules of these five boundary types leads to higher perceived privacy than not observing these conditions. Taken as a whole, observing communication channel, device, and business versus private purpose boundaries also leads to higher averages of perceived privacy across the whole sample. Theoretical and practical implications are discussed based on the results
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调和个性化与隐私的矛盾:探索在线个性化广告中的隐私边界
为了调和个性化与隐私的矛盾,我们采用隐私作为一种状态观,将隐私定义为一种信息边界规则遵循的状态。我们进一步确定了五种类型的边界,这些边界是维护隐私的一些重要隐含规则的基础:通信渠道、平台、设备、时间和目的边界。使用在线小插图调查,我们调查了这些边界类型如何影响用户的隐私感知,当他们受到个性化广告。使用固定效应和随机效应模型,我们研究了违反不同边界规则如何导致感知隐私的变化。我们的研究结果表明,这五种边界类型都是个体感知隐私的重要预测因子。在整个样本中,通信渠道、设备和业务与私人目的是感知隐私的重要预测因素。时间边界和平台边界在整个样本中与其他因素同时评估时均未达到统计学显著性。这意味着,对于每个人来说,遵守这五种边界类型的规则比不遵守这些条件能带来更高的感知隐私。作为一个整体,观察通信渠道、设备和商业与私人目的的界限也会导致整个样本中感知隐私的平均水平更高。在此基础上讨论了理论和实践意义
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来源期刊
Journal of the Association for Information Systems
Journal of the Association for Information Systems 工程技术-计算机:信息系统
CiteScore
11.20
自引率
5.20%
发文量
33
审稿时长
>12 weeks
期刊介绍: The Journal of the Association for Information Systems (JAIS), the flagship journal of the Association for Information Systems, publishes the highest quality scholarship in the field of information systems. It is inclusive in topics, level and unit of analysis, theory, method and philosophical and research approach, reflecting all aspects of Information Systems globally. The Journal promotes innovative, interesting and rigorously developed conceptual and empirical contributions and encourages theory based multi- or inter-disciplinary research.
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